Journal ArticleDOI
Multi-focus image fusion with a deep convolutional neural network
TLDR
A new multi-focus image fusion method is primarily proposed, aiming to learn a direct mapping between source images and focus map, using a deep convolutional neural network trained by high-quality image patches and their blurred versions to encode the mapping.About:
This article is published in Information Fusion.The article was published on 2017-07-01. It has received 826 citations till now. The article focuses on the topics: Image fusion & Convolutional neural network.read more
Citations
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Multifocal Image Fusion Based on Pseudo-Siamese Network and Spatial Frequency
Mei Wang,Manlu Liu,Yuzhu Xie +2 more
TL;DR: In this paper , a pseudo-Siamese network is used as an encoder to capture deep features in captured partially focused images, and the original decision-making image is obtained by filtering the extracted deep features through spatial frequency.
Journal ArticleDOI
A Fractional-Order Variation With a Novel Norm to Fuse Infrared and Visible Images
TL;DR: In this paper , a fractional-order variation with convolution norm is proposed for infrared and visible image fusion (IVIF), which can provide the structural group sparseness, alleviating the residual offset.
Journal ArticleDOI
A Novel MRI and PET Image Fusion in the NSST Domain Using YUV Color Space Based on Convolutional Neural Networks
Jinu Sebastian,G.R. Gnana King +1 more
Proceedings ArticleDOI
Infrared and Visible Image Fusion Method Based on Residual Block and Compression Decomposition Network
TL;DR: In this article , a self-encoder based on the residual structure is used to extract features, fuse features and reconstruct features from two source images to extract and fuse more effective feature information.
DGS-Fuse: unsupervised image fusion network combining global information
TL;DR: Zhang et al. as discussed by the authors proposed an encoder-decoder network, which combines pixel loss function, multiscale structural similarity loss function and total variation loss function to further reduce the detail loss in the image reconstruction process.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
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